Automatic detection and tracking of cells in microscopy images are major applications of computer vision technologies in both biomedical research and clinical practice. Though machine learning methods are increasingly common in these fields, classical algorithms still offer significant advantages for both tasks, including better explainability, faster computation, lower hardware requirements and more consistent performance. In this paper, we present a novel approach based on gravitational force fields that can compete with, and potentially outperform modern machine learning models when applied to fluorescence microscopy images. This method includes detection, segmentation, and tracking elements, with the results demonstrated on a Cell Tracking Challenge dataset.
{"title":"Gravitational cell detection and tracking in fluorescence microscopy data","authors":"Nikomidisz Eftimiu, Michal Kozubek","doi":"arxiv-2312.03509","DOIUrl":"https://doi.org/arxiv-2312.03509","url":null,"abstract":"Automatic detection and tracking of cells in microscopy images are major\u0000applications of computer vision technologies in both biomedical research and\u0000clinical practice. Though machine learning methods are increasingly common in\u0000these fields, classical algorithms still offer significant advantages for both\u0000tasks, including better explainability, faster computation, lower hardware\u0000requirements and more consistent performance. In this paper, we present a novel\u0000approach based on gravitational force fields that can compete with, and\u0000potentially outperform modern machine learning models when applied to\u0000fluorescence microscopy images. This method includes detection, segmentation,\u0000and tracking elements, with the results demonstrated on a Cell Tracking\u0000Challenge dataset.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138548340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seth Adler, Farzan Vahedifard, Rachel Akers, Christopher Sica, Mehmet Kocak, Edwin Moore, Marc Minkus, Gianna Elias, Nikhil Aggarwal, Sharon Byrd, Mehmoodur Rasheed, Robert S. Katz
Fibromyalgia is a complicated and multifaceted disorder marked by widespread chronic pain, fatigue, and muscle tenderness. Current explanations for the pathophysiology of this condition include the Central Sensitization Theory, Cytokine Inflammation Theory, Muscle Hypoxia, Muscle Tender Point Theory, and Small Fiber Neuropathy Theory. The objective of this review article is to examine and explain each of these current theories and to provide a background on our current understanding of fibromyalgia. The medical literature on this disorder, as well as on the roles of functional magnetic resonance imaging (fMRI) and elastography as diagnostic tools, was reviewed from the 1970s to early 2023, primarily using the PubMed database. Five prominent theories of fibromyalgia etiology were examined: 1) Central Sensitization Theory; 2) Cytokine Inflammation Theory; 3) Muscle Hypoxia; 4) Muscle Tender Point Theory; and 5) Small Fiber Neuropathy Theory. Previous fMRI studies of FMS have revealed two key findings. First, patients with FMS show altered activation patterns in brain regions involved in pain processing. Second, the connectivity between brain structures in individuals diagnosed with FMS and healthy controls is different. Both of these findings will be expanded upon in this paper. The article also explores the potential for future research in fibromyalgia due to the advancements in fMRI and elastography techniques, such as shear wave ultrasound. Increased understanding of the underlying mechanisms contributing to fibromyalgia symptoms is necessary for improved diagnosis and treatment, and advanced imaging techniques can aid in this process.
{"title":"Functional Magnetic Resonance Imaging Changes and Increased Muscle Pressure in Fibromyalgia: Insights from Prominent Theories of Pain and Muscle Imaging","authors":"Seth Adler, Farzan Vahedifard, Rachel Akers, Christopher Sica, Mehmet Kocak, Edwin Moore, Marc Minkus, Gianna Elias, Nikhil Aggarwal, Sharon Byrd, Mehmoodur Rasheed, Robert S. Katz","doi":"arxiv-2312.01788","DOIUrl":"https://doi.org/arxiv-2312.01788","url":null,"abstract":"Fibromyalgia is a complicated and multifaceted disorder marked by widespread\u0000chronic pain, fatigue, and muscle tenderness. Current explanations for the\u0000pathophysiology of this condition include the Central Sensitization Theory,\u0000Cytokine Inflammation Theory, Muscle Hypoxia, Muscle Tender Point Theory, and\u0000Small Fiber Neuropathy Theory. The objective of this review article is to\u0000examine and explain each of these current theories and to provide a background\u0000on our current understanding of fibromyalgia. The medical literature on this\u0000disorder, as well as on the roles of functional magnetic resonance imaging\u0000(fMRI) and elastography as diagnostic tools, was reviewed from the 1970s to\u0000early 2023, primarily using the PubMed database. Five prominent theories of\u0000fibromyalgia etiology were examined: 1) Central Sensitization Theory; 2)\u0000Cytokine Inflammation Theory; 3) Muscle Hypoxia; 4) Muscle Tender Point Theory;\u0000and 5) Small Fiber Neuropathy Theory. Previous fMRI studies of FMS have\u0000revealed two key findings. First, patients with FMS show altered activation\u0000patterns in brain regions involved in pain processing. Second, the connectivity\u0000between brain structures in individuals diagnosed with FMS and healthy controls\u0000is different. Both of these findings will be expanded upon in this paper. The article also explores the potential for future research in fibromyalgia\u0000due to the advancements in fMRI and elastography techniques, such as shear wave\u0000ultrasound. Increased understanding of the underlying mechanisms contributing\u0000to fibromyalgia symptoms is necessary for improved diagnosis and treatment, and\u0000advanced imaging techniques can aid in this process.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guilherme S. Y. Giardini, Gilberto L. Thomas, Carlo R. da Cunha, Rita M. C. de Almeida
The dynamics of single cell migration on flat surfaces is usually modeled by a Langevin-like problem consisting of ballistic motion for short periods and random walk. for long periods. Conversely, recent studies have revealed a previously neglected random motion at very short intervals, what would rule out the possibility of defining the cell instantaneous velocity and a robust measurement procedure. A previous attempt to address this issue considered an anisotropic migration model, which takes into account a polarization orientation along which the velocity is well-defined, and a direction orthogonal to the polarization vector that describes the random walk. Although the numerically and analytically calculated mean square displacement and auto-correlation agree with experimental data for that model, the velocity distribution peaks at zero, which contradicts experimental observations of a constant drift in the polarization direction. Moreover, Potts model simulations indicate that instantaneous velocity cannot be measured for any direction. Here, we consider dynamical equations for cell polarization, which is measurable and introduce a polarization-dependent displacement, circumventing the problem of ill defined instantaneous velocity. Polarization is a well-defined quantity, preserves memory for short intervals, and provides a robust measurement procedure for characterizing cell migration. We consider cell polarization dynamics to follow a modified Langevin equation that yields cell displacement distribution that peaks at positive values, in agreement with experiments and Potts model simulations. Furthermore, displacement autocorrelation functions present two different time scales, improving the agreement between theoretical fits and experiments or simulations.
{"title":"Velocities of Mesenchymal Cells May be Ill-Defined","authors":"Guilherme S. Y. Giardini, Gilberto L. Thomas, Carlo R. da Cunha, Rita M. C. de Almeida","doi":"arxiv-2311.17292","DOIUrl":"https://doi.org/arxiv-2311.17292","url":null,"abstract":"The dynamics of single cell migration on flat surfaces is usually modeled by\u0000a Langevin-like problem consisting of ballistic motion for short periods and\u0000random walk. for long periods. Conversely, recent studies have revealed a\u0000previously neglected random motion at very short intervals, what would rule out\u0000the possibility of defining the cell instantaneous velocity and a robust\u0000measurement procedure. A previous attempt to address this issue considered an\u0000anisotropic migration model, which takes into account a polarization\u0000orientation along which the velocity is well-defined, and a direction\u0000orthogonal to the polarization vector that describes the random walk. Although\u0000the numerically and analytically calculated mean square displacement and\u0000auto-correlation agree with experimental data for that model, the velocity\u0000distribution peaks at zero, which contradicts experimental observations of a\u0000constant drift in the polarization direction. Moreover, Potts model simulations\u0000indicate that instantaneous velocity cannot be measured for any direction.\u0000Here, we consider dynamical equations for cell polarization, which is\u0000measurable and introduce a polarization-dependent displacement, circumventing\u0000the problem of ill defined instantaneous velocity. Polarization is a\u0000well-defined quantity, preserves memory for short intervals, and provides a\u0000robust measurement procedure for characterizing cell migration. We consider\u0000cell polarization dynamics to follow a modified Langevin equation that yields\u0000cell displacement distribution that peaks at positive values, in agreement with\u0000experiments and Potts model simulations. Furthermore, displacement\u0000autocorrelation functions present two different time scales, improving the\u0000agreement between theoretical fits and experiments or simulations.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Experiments have shown that during the initial stage of Zebrafish morphogenesis a synchronous to asynchronous transition (SAT) occurs, as the cells divide extremely rapidly. In the synchronous phase, the cells divide in unison unlike in the asynchronous phase. Despite the widespread observation of SAT in experiments, a theory to calculate the critical number of cell cycles, $n^{*}$, at which asynchronous growth emerges does not exist. Here, using a model for the cell cycle, with the assumption that cell division times are Gaussian distributed with broadening, we predict $n^{*}$ and the time at which the SAT occurs. The theoretical results are in excellent agreement with experiments. The theory, supplemented by agent based simulations, establish that the SAT emerges as a consequence of biomechanical feedback on cell division. The emergence of asynchronous phase is due to linearly increasing fluctuations in the cell cycle times with each round of cell division. We also make several testable predictions, which would further shed light on the role of biomechanical feedback on the growth of multicellular systems.
{"title":"On the role of mechanical feedback in synchronous to asynchronous transition during embryogenesis","authors":"Abdul Malmi-Kakkada, Sumit Sinha, D. Thirumalai","doi":"arxiv-2311.18101","DOIUrl":"https://doi.org/arxiv-2311.18101","url":null,"abstract":"Experiments have shown that during the initial stage of Zebrafish\u0000morphogenesis a synchronous to asynchronous transition (SAT) occurs, as the\u0000cells divide extremely rapidly. In the synchronous phase, the cells divide in\u0000unison unlike in the asynchronous phase. Despite the widespread observation of\u0000SAT in experiments, a theory to calculate the critical number of cell cycles,\u0000$n^{*}$, at which asynchronous growth emerges does not exist. Here, using a\u0000model for the cell cycle, with the assumption that cell division times are\u0000Gaussian distributed with broadening, we predict $n^{*}$ and the time at which\u0000the SAT occurs. The theoretical results are in excellent agreement with\u0000experiments. The theory, supplemented by agent based simulations, establish\u0000that the SAT emerges as a consequence of biomechanical feedback on cell\u0000division. The emergence of asynchronous phase is due to linearly increasing\u0000fluctuations in the cell cycle times with each round of cell division. We also\u0000make several testable predictions, which would further shed light on the role\u0000of biomechanical feedback on the growth of multicellular systems.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Orr LevyDepartment of Immunobiology, Yale University School of Medicine, New Haven, CT, USAHoward Hughes Medical Institute, Chevy Chase, MD, USA, Shubham TripathiYale Center for Systems and Engineering Immunology and Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA, Scott D. PopeDepartment of Immunobiology, Yale University School of Medicine, New Haven, CT, USAHoward Hughes Medical Institute, Chevy Chase, MD, USA, Yang Y. LiuChanning Division of Network Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, Massachusetts, USACenter for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA, Ruslan MedzhitovDepartment of Immunobiology, Yale University School of Medicine, New Haven, CT, USAHoward Hughes Medical Institute, Chevy Chase, MD, USATananbaum Center for Theoretical and Analytical Human Biology, Yale University School of Medicine, New Haven, CT, USA
The diversity of expressed genes plays a critical role in cellular specialization, adaptation to environmental changes, and overall cell functionality. This diversity varies dramatically across cell types and is orchestrated by intricate, dynamic, and cell type-specific gene regulatory networks (GRNs). Despite extensive research on GRNs, their governing principles, as well as the underlying forces that have shaped them, remain largely unknown. Here, we investigated whether there is a tradeoff between the diversity of expressed genes and the intensity of GRN interactions. We have developed a computational framework that evaluates GRN interaction intensity from scRNA-seq data and used it to analyze simulated and real scRNA-seq data collected from different tissues in humans, mice, fruit flies, and C. elegans. We find a significant tradeoff between diversity and interaction intensity, driven by stability constraints, where the GRN could be stable up to a critical level of complexity - a product of gene expression diversity and interaction intensity. Furthermore, we analyzed hematopoietic stem cell differentiation data and find that the overall complexity of unstable transition states cells is higher than that of stem cells and fully differentiated cells. Our results suggest that GRNs are shaped by stability constraints which limit the diversity of gene expression.
{"title":"Gene regulatory interactions limit the gene expression diversity","authors":"Orr LevyDepartment of Immunobiology, Yale University School of Medicine, New Haven, CT, USAHoward Hughes Medical Institute, Chevy Chase, MD, USA, Shubham TripathiYale Center for Systems and Engineering Immunology and Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA, Scott D. PopeDepartment of Immunobiology, Yale University School of Medicine, New Haven, CT, USAHoward Hughes Medical Institute, Chevy Chase, MD, USA, Yang Y. LiuChanning Division of Network Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, Massachusetts, USACenter for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA, Ruslan MedzhitovDepartment of Immunobiology, Yale University School of Medicine, New Haven, CT, USAHoward Hughes Medical Institute, Chevy Chase, MD, USATananbaum Center for Theoretical and Analytical Human Biology, Yale University School of Medicine, New Haven, CT, USA","doi":"arxiv-2311.15503","DOIUrl":"https://doi.org/arxiv-2311.15503","url":null,"abstract":"The diversity of expressed genes plays a critical role in cellular\u0000specialization, adaptation to environmental changes, and overall cell\u0000functionality. This diversity varies dramatically across cell types and is\u0000orchestrated by intricate, dynamic, and cell type-specific gene regulatory\u0000networks (GRNs). Despite extensive research on GRNs, their governing\u0000principles, as well as the underlying forces that have shaped them, remain\u0000largely unknown. Here, we investigated whether there is a tradeoff between the\u0000diversity of expressed genes and the intensity of GRN interactions. We have\u0000developed a computational framework that evaluates GRN interaction intensity\u0000from scRNA-seq data and used it to analyze simulated and real scRNA-seq data\u0000collected from different tissues in humans, mice, fruit flies, and C. elegans.\u0000We find a significant tradeoff between diversity and interaction intensity,\u0000driven by stability constraints, where the GRN could be stable up to a critical\u0000level of complexity - a product of gene expression diversity and interaction\u0000intensity. Furthermore, we analyzed hematopoietic stem cell differentiation\u0000data and find that the overall complexity of unstable transition states cells\u0000is higher than that of stem cells and fully differentiated cells. Our results\u0000suggest that GRNs are shaped by stability constraints which limit the diversity\u0000of gene expression.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julia Sistermanns, Ellen Emken, Gregor Weirich, Oliver Hayden, Wolfgang Utschick
In the effort to aid cytologic diagnostics by establishing automatic single cell screening using high throughput digital holographic microscopy for clinical studies thousands of images and millions of cells are captured. The bottleneck lies in an automatic, fast, and unsupervised segmentation technique that does not limit the types of cells which might occur. We propose an unsupervised multistage method that segments correctly without confusing noise or reflections with cells and without missing cells that also includes the detection of relevant inner structures, especially the cell nucleus in the unstained cell. In an effort to make the information reasonable and interpretable for cytopathologists, we also introduce new cytoplasmic and nuclear features of potential help for cytologic diagnoses which exploit the quantitative phase information inherent to the measurement scheme. We show that the segmentation provides consistently good results over many experiments on patient samples in a reasonable per cell analysis time.
{"title":"Unsupervised high-throughput segmentation of cells and cell nuclei in quantitative phase images","authors":"Julia Sistermanns, Ellen Emken, Gregor Weirich, Oliver Hayden, Wolfgang Utschick","doi":"arxiv-2311.14639","DOIUrl":"https://doi.org/arxiv-2311.14639","url":null,"abstract":"In the effort to aid cytologic diagnostics by establishing automatic single\u0000cell screening using high throughput digital holographic microscopy for\u0000clinical studies thousands of images and millions of cells are captured. The\u0000bottleneck lies in an automatic, fast, and unsupervised segmentation technique\u0000that does not limit the types of cells which might occur. We propose an\u0000unsupervised multistage method that segments correctly without confusing noise\u0000or reflections with cells and without missing cells that also includes the\u0000detection of relevant inner structures, especially the cell nucleus in the\u0000unstained cell. In an effort to make the information reasonable and\u0000interpretable for cytopathologists, we also introduce new cytoplasmic and\u0000nuclear features of potential help for cytologic diagnoses which exploit the\u0000quantitative phase information inherent to the measurement scheme. We show that\u0000the segmentation provides consistently good results over many experiments on\u0000patient samples in a reasonable per cell analysis time.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell growth and gene expression, essential elements of all living systems, have long been the focus of biophysical interrogation. Advances in single-cell methods have invigorated theoretical studies into these processes. However, until recently, there was little dialog between the two areas of study. Most theoretical models for gene regulation assumed gene activity to be oblivious to the progression of the cell cycle between birth and division. But there are numerous ways in which the periodic character of all cellular observables can modulate gene expression. The molecular factors required for transcription and translation increase in number during the cell cycle, but are also diluted due to the continuous increase in cell volume. The replication of the genome changes the dosage of those same cellular players but also provides competing targets for regulatory binding. Finally, cell division reduces their number again, and so forth. Stochasticity is inherent to all these biological processes, manifested in fluctuations in the synthesis and degradation of new cellular components as well as the random partitioning of molecules at each cell division. The notion of gene expression as stationary is thus hard to justify. In this review, we survey the emerging paradigm of cell-cycle regulated gene expression, with an emphasis on the global expression patterns rather than gene-specific regulation. We discuss recent experimental reports where cell growth and gene expression were simultaneously measured in individual cells, providing first glimpses into the coupling between the two. While the experimental findings, not surprisingly, differ among genes and organisms, several theoretical models have emerged that attempt to reconcile these differences and form a unifying framework for understanding gene expression in growing cells.
{"title":"Gene expression in growing cells: A biophysical primer","authors":"Ido Golding, Ariel Amir","doi":"arxiv-2311.12143","DOIUrl":"https://doi.org/arxiv-2311.12143","url":null,"abstract":"Cell growth and gene expression, essential elements of all living systems,\u0000have long been the focus of biophysical interrogation. Advances in single-cell\u0000methods have invigorated theoretical studies into these processes. However,\u0000until recently, there was little dialog between the two areas of study. Most\u0000theoretical models for gene regulation assumed gene activity to be oblivious to\u0000the progression of the cell cycle between birth and division. But there are\u0000numerous ways in which the periodic character of all cellular observables can\u0000modulate gene expression. The molecular factors required for transcription and\u0000translation increase in number during the cell cycle, but are also diluted due\u0000to the continuous increase in cell volume. The replication of the genome\u0000changes the dosage of those same cellular players but also provides competing\u0000targets for regulatory binding. Finally, cell division reduces their number\u0000again, and so forth. Stochasticity is inherent to all these biological\u0000processes, manifested in fluctuations in the synthesis and degradation of new\u0000cellular components as well as the random partitioning of molecules at each\u0000cell division. The notion of gene expression as stationary is thus hard to\u0000justify. In this review, we survey the emerging paradigm of cell-cycle\u0000regulated gene expression, with an emphasis on the global expression patterns\u0000rather than gene-specific regulation. We discuss recent experimental reports\u0000where cell growth and gene expression were simultaneously measured in\u0000individual cells, providing first glimpses into the coupling between the two.\u0000While the experimental findings, not surprisingly, differ among genes and\u0000organisms, several theoretical models have emerged that attempt to reconcile\u0000these differences and form a unifying framework for understanding gene\u0000expression in growing cells.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study bacterial diffusion in disordered porous media. Interactions with obstacles, at unknown locations, make this problem challenging. We approach it by abstracting the environment to cell states with memoryless transitions. With this, we derive an effective diffusivity that agrees well with simulations in explicit geometries. The diffusivity is non-monotonic, and we solve the optimal run length. We also find a rescaling that causes all of the theory and simulations to collapse. Our results indicate that a small set of microscopic features captures bacterial diffusion in disordered media.
{"title":"Bacterial diffusion in disordered media, by forgetting the media","authors":"Henry H. Mattingly","doi":"arxiv-2311.10612","DOIUrl":"https://doi.org/arxiv-2311.10612","url":null,"abstract":"We study bacterial diffusion in disordered porous media. Interactions with\u0000obstacles, at unknown locations, make this problem challenging. We approach it\u0000by abstracting the environment to cell states with memoryless transitions. With\u0000this, we derive an effective diffusivity that agrees well with simulations in\u0000explicit geometries. The diffusivity is non-monotonic, and we solve the optimal\u0000run length. We also find a rescaling that causes all of the theory and\u0000simulations to collapse. Our results indicate that a small set of microscopic\u0000features captures bacterial diffusion in disordered media.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"740 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kevin Troulé, Robert Petryszak, Martin Prete, James Cranley, Alicia Harasty, Zewen Kelvin Tuong, Sarah A Teichmann, Luz Garcia-Alonso, Roser Vento-Tormo
Cell-cell communication is essential for tissue development, regeneration and function, and its disruption can lead to diseases and developmental abnormalities. The revolution of single-cell genomics technologies offers unprecedented insights into cellular identities, opening new avenues to resolve the intricate cellular interactions present in tissue niches. CellPhoneDB is a bioinformatics toolkit designed to infer cell-cell communication by combining a curated repository of bona fide ligand-receptor interactions with a set of computational and statistical methods to integrate them with single-cell genomics data. Importantly, CellPhoneDB captures the multimeric nature of molecular complexes, thus representing cell-cell communication biology faithfully. Here we present CellPhoneDB v5, an updated version of the tool, which offers several new features. Firstly, the repository has been expanded by one-third with the addition of new interactions. These encompass interactions mediated by non-protein ligands such as endocrine hormones and GPCR ligands. Secondly, it includes a differentially expression-based methodology for more tailored interaction queries. Thirdly, it incorporates novel computational methods to prioritise specific cell-cell interactions, leveraging other single-cell modalities, such as spatial information or TF activities (i.e. CellSign module). Finally, we provide CellPhoneDBViz, a module to interactively visualise and share results amongst users. Altogether, CellPhoneDB v5 elevates the precision of cell-cell communication inference, ushering in new perspectives to comprehend tissue biology in both healthy and pathological states.
{"title":"CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data","authors":"Kevin Troulé, Robert Petryszak, Martin Prete, James Cranley, Alicia Harasty, Zewen Kelvin Tuong, Sarah A Teichmann, Luz Garcia-Alonso, Roser Vento-Tormo","doi":"arxiv-2311.04567","DOIUrl":"https://doi.org/arxiv-2311.04567","url":null,"abstract":"Cell-cell communication is essential for tissue development, regeneration and\u0000function, and its disruption can lead to diseases and developmental\u0000abnormalities. The revolution of single-cell genomics technologies offers\u0000unprecedented insights into cellular identities, opening new avenues to resolve\u0000the intricate cellular interactions present in tissue niches. CellPhoneDB is a\u0000bioinformatics toolkit designed to infer cell-cell communication by combining a\u0000curated repository of bona fide ligand-receptor interactions with a set of\u0000computational and statistical methods to integrate them with single-cell\u0000genomics data. Importantly, CellPhoneDB captures the multimeric nature of\u0000molecular complexes, thus representing cell-cell communication biology\u0000faithfully. Here we present CellPhoneDB v5, an updated version of the tool,\u0000which offers several new features. Firstly, the repository has been expanded by\u0000one-third with the addition of new interactions. These encompass interactions\u0000mediated by non-protein ligands such as endocrine hormones and GPCR ligands.\u0000Secondly, it includes a differentially expression-based methodology for more\u0000tailored interaction queries. Thirdly, it incorporates novel computational\u0000methods to prioritise specific cell-cell interactions, leveraging other\u0000single-cell modalities, such as spatial information or TF activities (i.e.\u0000CellSign module). Finally, we provide CellPhoneDBViz, a module to interactively\u0000visualise and share results amongst users. Altogether, CellPhoneDB v5 elevates\u0000the precision of cell-cell communication inference, ushering in new\u0000perspectives to comprehend tissue biology in both healthy and pathological\u0000states.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cells that collide with each other repolarize away from contact, in a process called contact inhibition of locomotion (CIL), which is necessary for correct development of the embryo. CIL can occur even when cells make a micron-scale contact with a neighbor - much smaller than their size. How precisely can a cell sense cell-cell contact and repolarize in the correct direction? What factors control whether a cell recognizes it has contacted a neighbor? We propose a theoretical model for the limits of CIL where cells recognize the presence of another cell by binding the protein ephrin with the Eph receptor. This recognition is made difficult by the presence of interfering ligands that bind nonspecifically. Both theoretical predictions and simulation results show that it becomes more difficult to sense cell-cell contact when it is difficult to distinguish ephrin from the interfering ligands, or when there are more interfering ligands, or when the contact width decreases. However, the error of estimating contact position remains almost constant when the contact width changes. This happens because the cell gains spatial information largely from the boundaries of cell-cell contact. We study using statistical decision theory the likelihood of a false positive CIL event in the absence of cell-cell contact, and the likelihood of a false negative where CIL does not occur when another cell is present. Our results suggest that the cell is more likely to make incorrect decisions when the contact width is very small or so large that it nears the cell's perimeter. However, in general, we find that cells have the ability to make reasonably reliable CIL decisions even for very narrow (micron-scale) contacts, even if the concentration of interfering ligands is ten times that of the correct ligands.
{"title":"Limits on the accuracy of contact inhibition of locomotion","authors":"Wei Wang, Brian A. Camley","doi":"arxiv-2311.00085","DOIUrl":"https://doi.org/arxiv-2311.00085","url":null,"abstract":"Cells that collide with each other repolarize away from contact, in a process\u0000called contact inhibition of locomotion (CIL), which is necessary for correct\u0000development of the embryo. CIL can occur even when cells make a micron-scale\u0000contact with a neighbor - much smaller than their size. How precisely can a\u0000cell sense cell-cell contact and repolarize in the correct direction? What\u0000factors control whether a cell recognizes it has contacted a neighbor? We\u0000propose a theoretical model for the limits of CIL where cells recognize the\u0000presence of another cell by binding the protein ephrin with the Eph receptor.\u0000This recognition is made difficult by the presence of interfering ligands that\u0000bind nonspecifically. Both theoretical predictions and simulation results show\u0000that it becomes more difficult to sense cell-cell contact when it is difficult\u0000to distinguish ephrin from the interfering ligands, or when there are more\u0000interfering ligands, or when the contact width decreases. However, the error of\u0000estimating contact position remains almost constant when the contact width\u0000changes. This happens because the cell gains spatial information largely from\u0000the boundaries of cell-cell contact. We study using statistical decision theory\u0000the likelihood of a false positive CIL event in the absence of cell-cell\u0000contact, and the likelihood of a false negative where CIL does not occur when\u0000another cell is present. Our results suggest that the cell is more likely to\u0000make incorrect decisions when the contact width is very small or so large that\u0000it nears the cell's perimeter. However, in general, we find that cells have the\u0000ability to make reasonably reliable CIL decisions even for very narrow\u0000(micron-scale) contacts, even if the concentration of interfering ligands is\u0000ten times that of the correct ligands.","PeriodicalId":501321,"journal":{"name":"arXiv - QuanBio - Cell Behavior","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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